A hybrid variational framework for AIS data quality enhancement and anomaly detection

Automatic Identification System (AIS) data plays a critical role in maritime analytics; however, AIS trajectories frequently suffer from noise, inconsistencies, and missing values that degrade their analytical reliability. To address these challenges, this study proposes the Deep Quality Vessel Trajectory Inspector (DQTI), a hybrid variational framework for AIS data quality enhancement and anomaly detection. The proposed model integrates a Variational Recurrent Neural Network (VRNN) with a learnable fusion of GRU and LSTM units, enabling effective modeling of both short-term motion continuity and long-range temporal dependencies in vessel trajectories. A four-hot encoding scheme is adopted to represent longitude, latitude, speed over ground, and course over ground, providing a structured and noise-tolerant representation of multivariate maritime signals. Anomaly detection is performed through a reconstruction-based mechanism that identifies inconsistent AIS messages by measuring deviations between observed and reconstructed trajectories. The experimental evaluation is conducted on more than 141,000 AIS records collected from 800 vessels in the Red Sea. Results demonstrate improved reconstruction accuracy, reduced KL divergence, and more reliable anomaly discrimination compared to baseline models. These findings highlight the effectiveness of data representations and hybrid variational recurrent architectures for enhancing AIS data quality in complex and noisy sequential datasets.